MATS Summer 2026
The Summer 2026 program will run from June through August. It will be largest MATS program to date with 120 fellows and 100 mentors. Fellows will be connected with mentors or organizational research groups, such as Anthropic's Alignment Science team, UK AISI, Redwood Research, ARC, and LawZero, to collaborate on a research project over the summer. Some fellows will be offered a 6+ month extension to continue this collaboration.
Applications are now closed.
Program phases
Key dates for the application and admissions timeline
Applications typically open several months before the program begins. Applicants complete a multi-stage admissions process, beginning with a general application. Depending on the tracks, streams, and mentors they apply to, applicants may also complete additional evaluations such as interviews, work tests, coding assessments, or writing samples before final admissions decisions are made.
Each MATS stream brings together scholars and mentors around a shared research agenda. Streams vary in methodology and focus area, spanning topics such as interpretability, control, evaluations, governance, cybersecurity, and agent foundations.
This stream will focus on AI control and evaluations of dangerous capabilities, propensities, and alignment.
Implementing SL4/5 and searching for differentially defense-favored security tools.
This stream will pursue research on securing and hardening AI systems through rigorous testing, provable defenses, and formal specification, including improving benchmarks for agentic security, scaling mathematically-grounded robustness techniques like randomized smoothing and Lipschitz-constrained training, and developing formal methods for specifying safe agent behaviors.
We are excited to supervise projects that fall within the two following categories:
- Studying the causes, implications, and mitigations of [instances of] situational awareness;
- Contributing directly to LawZero's Scientist AI.
For 1., we are particularly interested in:
- Evaluation / monitorability awareness;
- Self-awareness, in an introspective sense.
For 2., we are especially interested in:
- Testing if "truth-ification" (a process that, given a corpus of text, augments it so as to make sources of information explicit) allows language models to generalize better;
- Developing amortized inference methods to estimate the uncertainty of a predictor (such as an autoregressive model).
Lee's stream will focus primarily on improving mechanistic interpretability methods for reverse-engineering neural networks.
This stream will work on projects that empirically assess national security threats of AI misuse (CBRN terrorism and cyberattacks) and improve dangerous capability evaluations. Threat modeling applicants should have a skeptical mindset, enjoy case study work, and be strong written communicators. Eval applicants should be able and excited to help demonstrate concepts like sandbagging elicitation gaps in an AI misuse context.
Priority directions:
- Risks from automating AI research
- Automating safety and alignment research
- AGI privacy
- Measuring long-horizon agentic capabilities
- New alignment methods
- Science of post-training